Overview

Dataset statistics

Number of variables31
Number of observations50000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 MiB
Average record size in memory277.1 B

Variable types

Numeric18
Categorical13

Alerts

Amount_invested_monthly is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Annual_Income is highly overall correlated with Amount_invested_monthly and 2 other fieldsHigh correlation
Credit_History_Months is highly overall correlated with Interest_Rate and 4 other fieldsHigh correlation
Credit_Mix is highly overall correlated with Delay_from_due_date and 6 other fieldsHigh correlation
Delay_from_due_date is highly overall correlated with Credit_Mix and 4 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Credit_History_Months and 6 other fieldsHigh correlation
Monthly_Balance is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Monthly_Inhand_Salary is highly overall correlated with Amount_invested_monthly and 2 other fieldsHigh correlation
Num_Bank_Accounts is highly overall correlated with Credit_Mix and 3 other fieldsHigh correlation
Num_Credit_Inquiries is highly overall correlated with Credit_History_Months and 2 other fieldsHigh correlation
Num_of_Delayed_Payment is highly overall correlated with Credit_Mix and 3 other fieldsHigh correlation
Num_of_Loan is highly overall correlated with Credit_History_Months and 3 other fieldsHigh correlation
Num_of_Loan_Types is highly overall correlated with Credit_History_Months and 2 other fieldsHigh correlation
Outstanding_Debt is highly overall correlated with Credit_History_Months and 6 other fieldsHigh correlation
Payment_Behaviour_lavel is highly overall correlated with Payment_Behaviour_sizeHigh correlation
Payment_Behaviour_size is highly overall correlated with Payment_Behaviour_lavelHigh correlation
Payment_of_Min_Amount is highly overall correlated with Credit_MixHigh correlation
Credit_Utilization_Ratio has unique valuesUnique
Num_Bank_Accounts has 2166 (4.3%) zerosZeros
Num_of_Loan has 5163 (10.3%) zerosZeros
Num_of_Loan_Types has 6484 (13.0%) zerosZeros
Delay_from_due_date has 626 (1.3%) zerosZeros
Num_of_Delayed_Payment has 784 (1.6%) zerosZeros
Num_Credit_Inquiries has 1102 (2.2%) zerosZeros
Total_EMI_per_month has 5002 (10.0%) zerosZeros

Reproduction

Analysis started2025-12-10 06:23:58.848342
Analysis finished2025-12-10 06:25:06.607263
Duration1 minute and 7.76 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9489
Minimum19
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:06.779574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile20
Q128
median34
Q341
95-th percentile52
Maximum56
Range37
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2970327
Coefficient of variation (CV)0.26601789
Kurtosis-0.59152144
Mean34.9489
Median Absolute Deviation (MAD)7
Skewness0.28176638
Sum1747445
Variance86.434817
MonotonicityNot monotonic
2025-12-10T12:25:06.945027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
348246
 
16.5%
391493
 
3.0%
321440
 
2.9%
441428
 
2.9%
221422
 
2.8%
351414
 
2.8%
371397
 
2.8%
271382
 
2.8%
201374
 
2.7%
291368
 
2.7%
Other values (28)29036
58.1%
ValueCountFrequency (%)
191277
2.6%
201374
2.7%
211260
2.5%
221422
2.8%
231213
2.4%
241318
2.6%
251325
2.6%
261348
2.7%
271382
2.8%
281344
2.7%
ValueCountFrequency (%)
56498
1.0%
55647
1.3%
54623
1.2%
53657
1.3%
52593
1.2%
51610
1.2%
50653
1.3%
49644
1.3%
48593
1.2%
47624
1.2%

Occupation
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Unknown
3438 
Lawyer
 
3324
Engineer
 
3212
Architect
 
3195
Mechanic
 
3168
Other values (11)
33663 

Length

Max length13
Median length10
Mean length8.43476
Min length6

Characters and Unicode

Total characters421738
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScientist
2nd rowScientist
3rd rowScientist
4th rowScientist
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown3438
 
6.9%
Lawyer3324
 
6.6%
Engineer3212
 
6.4%
Architect3195
 
6.4%
Mechanic3168
 
6.3%
Developer3146
 
6.3%
Accountant3133
 
6.3%
Media_Manager3130
 
6.3%
Scientist3104
 
6.2%
Teacher3103
 
6.2%
Other values (6)18047
36.1%

Length

2025-12-10T12:25:07.130566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown3438
 
6.9%
lawyer3324
 
6.6%
engineer3212
 
6.4%
architect3195
 
6.4%
mechanic3168
 
6.3%
developer3146
 
6.3%
accountant3133
 
6.3%
media_manager3130
 
6.3%
scientist3104
 
6.2%
teacher3103
 
6.2%
Other values (6)18047
36.1%

Most occurring characters

ValueCountFrequency (%)
e56361
13.4%
n47596
11.3%
r43349
10.3%
a34102
 
8.1%
c31173
 
7.4%
t30964
 
7.3%
i30777
 
7.3%
o18808
 
4.5%
M15375
 
3.6%
u12220
 
2.9%
Other values (20)101013
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)421738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e56361
13.4%
n47596
11.3%
r43349
10.3%
a34102
 
8.1%
c31173
 
7.4%
t30964
 
7.3%
i30777
 
7.3%
o18808
 
4.5%
M15375
 
3.6%
u12220
 
2.9%
Other values (20)101013
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)421738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e56361
13.4%
n47596
11.3%
r43349
10.3%
a34102
 
8.1%
c31173
 
7.4%
t30964
 
7.3%
i30777
 
7.3%
o18808
 
4.5%
M15375
 
3.6%
u12220
 
2.9%
Other values (20)101013
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)421738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e56361
13.4%
n47596
11.3%
r43349
10.3%
a34102
 
8.1%
c31173
 
7.4%
t30964
 
7.3%
i30777
 
7.3%
o18808
 
4.5%
M15375
 
3.6%
u12220
 
2.9%
Other values (20)101013
24.0%

Annual_Income
Real number (ℝ)

High correlation 

Distinct12954
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156138.03
Minimum7005.93
Maximum24137255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:07.309151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7005.93
5-th percentile9888.7
Q120062.86
median37575.87
Q369955.56
95-th percentile132753.52
Maximum24137255
Range24130249
Interquartile range (IQR)49892.7

Descriptive statistics

Standard deviation1294277.9
Coefficient of variation (CV)8.2893187
Kurtosis206.052
Mean156138.03
Median Absolute Deviation (MAD)20453.5
Skewness13.887611
Sum7.8069016 × 109
Variance1.6751553 × 1012
MonotonicityNot monotonic
2025-12-10T12:25:07.514244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37575.873520
 
7.0%
36585.128
 
< 0.1%
109945.328
 
< 0.1%
9141.638
 
< 0.1%
17816.758
 
< 0.1%
22434.168
 
< 0.1%
72524.28
 
< 0.1%
95596.358
 
< 0.1%
40341.167
 
< 0.1%
20867.677
 
< 0.1%
Other values (12944)46410
92.8%
ValueCountFrequency (%)
7005.934
< 0.1%
7006.0353
< 0.1%
7006.524
< 0.1%
7011.6854
< 0.1%
7012.314
< 0.1%
7019.4354
< 0.1%
7020.5453
< 0.1%
7021.914
< 0.1%
7023.163
< 0.1%
7039.7454
< 0.1%
ValueCountFrequency (%)
241372551
< 0.1%
241218321
< 0.1%
241123041
< 0.1%
240173071
< 0.1%
240040881
< 0.1%
239942431
< 0.1%
239677131
< 0.1%
239608951
< 0.1%
239412241
< 0.1%
238754131
< 0.1%

Monthly_Inhand_Salary
Real number (ℝ)

High correlation 

Distinct12794
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4017.6932
Minimum303.64542
Maximum15204.633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:07.715692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum303.64542
5-th percentile887.24298
Q11794.3042
median3086.305
Q35338.9675
95-th percentile10434.411
Maximum15204.633
Range14900.988
Interquartile range (IQR)3544.6633

Descriptive statistics

Standard deviation2952.4799
Coefficient of variation (CV)0.73486943
Kurtosis1.4053214
Mean4017.6932
Median Absolute Deviation (MAD)1538.0592
Skewness1.3544086
Sum2.0088466 × 108
Variance8717137.6
MonotonicityNot monotonic
2025-12-10T12:25:07.930872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3086.3057498
 
15.0%
1315.5608338
 
< 0.1%
536.431257
 
< 0.1%
5766.4916677
 
< 0.1%
4387.27257
 
< 0.1%
6082.18757
 
< 0.1%
3080.5557
 
< 0.1%
6639.567
 
< 0.1%
2295.0583337
 
< 0.1%
6358.9566676
 
< 0.1%
Other values (12784)42439
84.9%
ValueCountFrequency (%)
303.64541672
< 0.1%
319.556254
< 0.1%
331.03192332
< 0.1%
332.12833333
< 0.1%
332.431254
< 0.1%
333.59666674
< 0.1%
355.20833334
< 0.1%
357.25583334
< 0.1%
358.05833334
< 0.1%
361.60333334
< 0.1%
ValueCountFrequency (%)
15204.633333
< 0.1%
15167.184
< 0.1%
15136.696673
< 0.1%
15115.193
< 0.1%
15101.943
< 0.1%
15090.076674
< 0.1%
15066.783334
< 0.1%
14978.336673
< 0.1%
14960.251
 
< 0.1%
14929.543
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3777
Minimum0
Maximum11
Zeros2166
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:08.096605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5771953
Coefficient of variation (CV)0.47923746
Kurtosis-0.66244139
Mean5.3777
Median Absolute Deviation (MAD)2
Skewness-0.19944736
Sum268885
Variance6.6419355
MonotonicityNot monotonic
2025-12-10T12:25:08.258498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
67155
14.3%
76408
12.8%
86387
12.8%
46100
12.2%
56068
12.1%
35955
11.9%
92738
 
5.5%
102599
 
5.2%
12253
 
4.5%
02166
 
4.3%
Other values (2)2171
 
4.3%
ValueCountFrequency (%)
02166
 
4.3%
12253
 
4.5%
22152
 
4.3%
35955
11.9%
46100
12.2%
56068
12.1%
67155
14.3%
76408
12.8%
86387
12.8%
92738
 
5.5%
ValueCountFrequency (%)
1119
 
< 0.1%
102599
 
5.2%
92738
 
5.5%
86387
12.8%
76408
12.8%
67155
14.3%
56068
12.1%
46100
12.2%
35955
11.9%
22152
 
4.3%

Num_Credit_Card
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.52008
Minimum0
Maximum11
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:08.398658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q37
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0495075
Coefficient of variation (CV)0.3712822
Kurtosis-0.26021895
Mean5.52008
Median Absolute Deviation (MAD)1
Skewness0.24616653
Sum276004
Variance4.2004808
MonotonicityNot monotonic
2025-12-10T12:25:08.537968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
510389
20.8%
78271
16.5%
68243
16.5%
47072
14.1%
36539
13.1%
82497
 
5.0%
102405
 
4.8%
92333
 
4.7%
21131
 
2.3%
11063
 
2.1%
Other values (2)57
 
0.1%
ValueCountFrequency (%)
016
 
< 0.1%
11063
 
2.1%
21131
 
2.3%
36539
13.1%
47072
14.1%
510389
20.8%
68243
16.5%
78271
16.5%
82497
 
5.0%
92333
 
4.7%
ValueCountFrequency (%)
1141
 
0.1%
102405
 
4.8%
92333
 
4.7%
82497
 
5.0%
78271
16.5%
68243
16.5%
510389
20.8%
47072
14.1%
36539
13.1%
21131
 
2.3%

Interest_Rate
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.50196
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:08.697190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median13
Q320
95-th percentile31
Maximum34
Range33
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.6613727
Coefficient of variation (CV)0.59725531
Kurtosis-0.63386875
Mean14.50196
Median Absolute Deviation (MAD)6
Skewness0.51047292
Sum725098
Variance75.019377
MonotonicityNot monotonic
2025-12-10T12:25:08.881597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
82503
 
5.0%
52500
 
5.0%
62368
 
4.7%
122288
 
4.6%
102259
 
4.5%
92253
 
4.5%
72250
 
4.5%
112198
 
4.4%
132153
 
4.3%
182052
 
4.1%
Other values (24)27176
54.4%
ValueCountFrequency (%)
11344
2.7%
21245
2.5%
31388
2.8%
41287
2.6%
52500
5.0%
62368
4.7%
72250
4.5%
82503
5.0%
92253
4.5%
102259
4.5%
ValueCountFrequency (%)
34744
1.5%
33734
1.5%
32874
1.7%
31731
1.5%
30846
1.7%
29833
1.7%
28815
1.6%
27808
1.6%
26749
1.5%
25790
1.6%

Num_of_Loan
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4839
Minimum0
Maximum9
Zeros5163
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:09.031854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3340115
Coefficient of variation (CV)0.66994217
Kurtosis-0.35125905
Mean3.4839
Median Absolute Deviation (MAD)1
Skewness0.5269563
Sum174195
Variance5.4476097
MonotonicityNot monotonic
2025-12-10T12:25:09.162994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
311774
23.5%
27173
14.3%
46982
14.0%
05163
10.3%
15029
10.1%
63707
 
7.4%
73483
 
7.0%
53437
 
6.9%
91746
 
3.5%
81506
 
3.0%
ValueCountFrequency (%)
05163
10.3%
15029
10.1%
27173
14.3%
311774
23.5%
46982
14.0%
53437
 
6.9%
63707
 
7.4%
73483
 
7.0%
81506
 
3.0%
91746
 
3.5%
ValueCountFrequency (%)
91746
 
3.5%
81506
 
3.0%
73483
 
7.0%
63707
 
7.4%
53437
 
6.9%
46982
14.0%
311774
23.5%
27173
14.3%
15029
10.1%
05163
10.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34480 
1
15520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Length

2025-12-10T12:25:09.322984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:09.431083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring characters

ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Home Equity Loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34300 
1
15700 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
034300
68.6%
115700
31.4%

Length

2025-12-10T12:25:09.563312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:09.674443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034300
68.6%
115700
31.4%

Most occurring characters

ValueCountFrequency (%)
034300
68.6%
115700
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034300
68.6%
115700
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034300
68.6%
115700
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034300
68.6%
115700
31.4%

Student Loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34480 
1
15520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Length

2025-12-10T12:25:09.805512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:09.914647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring characters

ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034480
69.0%
115520
31.0%

Payday Loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34028 
1
15972 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034028
68.1%
115972
31.9%

Length

2025-12-10T12:25:10.045797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:10.147222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034028
68.1%
115972
31.9%

Most occurring characters

ValueCountFrequency (%)
034028
68.1%
115972
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034028
68.1%
115972
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034028
68.1%
115972
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034028
68.1%
115972
31.9%

Personal Loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34448 
1
15552 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
034448
68.9%
115552
31.1%

Length

2025-12-10T12:25:10.287498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:10.397072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034448
68.9%
115552
31.1%

Most occurring characters

ValueCountFrequency (%)
034448
68.9%
115552
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034448
68.9%
115552
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034448
68.9%
115552
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034448
68.9%
115552
31.1%

Auto Loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34720 
1
15280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
034720
69.4%
115280
30.6%

Length

2025-12-10T12:25:10.527917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:10.636552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034720
69.4%
115280
30.6%

Most occurring characters

ValueCountFrequency (%)
034720
69.4%
115280
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034720
69.4%
115280
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034720
69.4%
115280
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034720
69.4%
115280
30.6%

Mortgage Loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34320 
1
15680 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034320
68.6%
115680
31.4%

Length

2025-12-10T12:25:10.768199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:10.881031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034320
68.6%
115680
31.4%

Most occurring characters

ValueCountFrequency (%)
034320
68.6%
115680
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034320
68.6%
115680
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034320
68.6%
115680
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034320
68.6%
115680
31.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
34136 
1
15864 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
034136
68.3%
115864
31.7%

Length

2025-12-10T12:25:11.010800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:11.113516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
034136
68.3%
115864
31.7%

Most occurring characters

ValueCountFrequency (%)
034136
68.3%
115864
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
034136
68.3%
115864
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
034136
68.3%
115864
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
034136
68.3%
115864
31.7%

Num_of_Loan_Types
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.50176
Minimum0
Maximum7
Zeros6484
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:11.213182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.626865
Coefficient of variation (CV)0.65028821
Kurtosis-0.60423256
Mean2.50176
Median Absolute Deviation (MAD)1
Skewness0.25031553
Sum125088
Variance2.6466898
MonotonicityNot monotonic
2025-12-10T12:25:11.330086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
211328
22.7%
310532
21.1%
18016
16.0%
47416
14.8%
06484
13.0%
54440
 
8.9%
61532
 
3.1%
7252
 
0.5%
ValueCountFrequency (%)
06484
13.0%
18016
16.0%
211328
22.7%
310532
21.1%
47416
14.8%
54440
 
8.9%
61532
 
3.1%
7252
 
0.5%
ValueCountFrequency (%)
7252
 
0.5%
61532
 
3.1%
54440
 
8.9%
47416
14.8%
310532
21.1%
211328
22.7%
18016
16.0%
06484
13.0%

Delay_from_due_date
Real number (ℝ)

High correlation  Zeros 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.05264
Minimum-5
Maximum67
Zeros626
Zeros (%)1.3%
Negative298
Negative (%)0.6%
Memory size1.8 MiB
2025-12-10T12:25:11.498096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.860397
Coefficient of variation (CV)0.70586859
Kurtosis0.3444274
Mean21.05264
Median Absolute Deviation (MAD)9
Skewness0.96492811
Sum1052632
Variance220.83141
MonotonicityNot monotonic
2025-12-10T12:25:11.696635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131761
 
3.5%
151759
 
3.5%
81680
 
3.4%
91656
 
3.3%
101645
 
3.3%
141636
 
3.3%
121625
 
3.2%
71587
 
3.2%
61584
 
3.2%
111573
 
3.1%
Other values (63)33494
67.0%
ValueCountFrequency (%)
-518
 
< 0.1%
-449
 
0.1%
-359
 
0.1%
-271
 
0.1%
-1101
 
0.2%
0626
1.3%
1668
1.3%
2669
1.3%
3848
1.7%
4825
1.7%
ValueCountFrequency (%)
677
 
< 0.1%
6612
 
< 0.1%
6530
 
0.1%
6433
 
0.1%
6321
 
< 0.1%
62279
0.6%
61271
0.5%
60259
0.5%
59250
0.5%
58282
0.6%

Num_of_Delayed_Payment
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.4874
Minimum0
Maximum28
Zeros784
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:11.879865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median14
Q318
95-th percentile23
Maximum28
Range28
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.8535089
Coefficient of variation (CV)0.43399832
Kurtosis-0.36112928
Mean13.4874
Median Absolute Deviation (MAD)4
Skewness-0.22428142
Sum674370
Variance34.263567
MonotonicityNot monotonic
2025-12-10T12:25:12.046831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
147588
 
15.2%
192622
 
5.2%
152594
 
5.2%
182570
 
5.1%
162548
 
5.1%
172545
 
5.1%
102517
 
5.0%
122483
 
5.0%
112440
 
4.9%
202422
 
4.8%
Other values (19)19671
39.3%
ValueCountFrequency (%)
0784
 
1.6%
1814
 
1.6%
2872
 
1.7%
3939
 
1.9%
4887
 
1.8%
51036
2.1%
61076
2.2%
71140
2.3%
82352
4.7%
92365
4.7%
ValueCountFrequency (%)
2864
 
0.1%
27104
 
0.2%
26147
 
0.3%
25813
 
1.6%
24836
 
1.7%
231011
 
2.0%
221116
2.2%
211315
2.6%
202422
4.8%
192622
5.2%

Changed_Credit_Limit
Real number (ℝ)

Distinct3713
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.101752
Minimum-6.45
Maximum28.32
Zeros2
Zeros (%)< 0.1%
Negative835
Negative (%)1.7%
Memory size1.8 MiB
2025-12-10T12:25:12.484576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.45
5-th percentile1.17
Q15.44
median9.41
Q314.14
95-th percentile21.91
Maximum28.32
Range34.77
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation6.3479675
Coefficient of variation (CV)0.62840265
Kurtosis-0.069241267
Mean10.101752
Median Absolute Deviation (MAD)4.27
Skewness0.52548721
Sum505087.58
Variance40.296692
MonotonicityNot monotonic
2025-12-10T12:25:12.677253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.411719
 
3.4%
11.570
 
0.1%
11.3263
 
0.1%
7.0160
 
0.1%
7.3560
 
0.1%
10.0657
 
0.1%
7.6356
 
0.1%
8.2256
 
0.1%
7.6956
 
0.1%
8.8255
 
0.1%
Other values (3703)47748
95.5%
ValueCountFrequency (%)
-6.452
< 0.1%
-6.431
< 0.1%
-6.411
< 0.1%
-6.41
< 0.1%
-6.371
< 0.1%
-6.351
< 0.1%
-6.331
< 0.1%
-6.321
< 0.1%
-6.31
< 0.1%
-6.291
< 0.1%
ValueCountFrequency (%)
28.322
 
< 0.1%
28.311
 
< 0.1%
28.35
< 0.1%
28.287
< 0.1%
28.272
 
< 0.1%
28.263
< 0.1%
28.251
 
< 0.1%
28.234
< 0.1%
28.221
 
< 0.1%
28.212
 
< 0.1%

Num_Credit_Inquiries
Real number (ℝ)

High correlation  Zeros 

Distinct750
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.60244
Minimum0
Maximum2593
Zeros1102
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:12.874835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile15
Maximum2593
Range2593
Interquartile range (IQR)6

Descriptive statistics

Standard deviation194.96233
Coefficient of variation (CV)6.5860223
Kurtosis98.50741
Mean29.60244
Median Absolute Deviation (MAD)3
Skewness9.6911506
Sum1480122
Variance38010.31
MonotonicityNot monotonic
2025-12-10T12:25:13.077195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75330
10.7%
54709
9.4%
44402
 
8.8%
64375
 
8.8%
83922
 
7.8%
93523
 
7.0%
33466
 
6.9%
112996
 
6.0%
102982
 
6.0%
122585
 
5.2%
Other values (740)11710
23.4%
ValueCountFrequency (%)
01102
 
2.2%
11747
 
3.5%
22454
4.9%
33466
6.9%
44402
8.8%
54709
9.4%
64375
8.8%
75330
10.7%
83922
7.8%
93523
7.0%
ValueCountFrequency (%)
25931
< 0.1%
25921
< 0.1%
25881
< 0.1%
25861
< 0.1%
25831
< 0.1%
25761
< 0.1%
25751
< 0.1%
25741
< 0.1%
25701
< 0.1%
25671
< 0.1%

Outstanding_Debt
Real number (ℝ)

High correlation 

Distinct12203
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1422.9254
Minimum0.23
Maximum4998.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:13.274934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile120.02
Q1570.51
median1164.47
Q31931.12
95-th percentile4071.62
Maximum4998.07
Range4997.84
Interquartile range (IQR)1360.61

Descriptive statistics

Standard deviation1149.914
Coefficient of variation (CV)0.8081337
Kurtosis0.95092845
Mean1422.9254
Median Absolute Deviation (MAD)633.96
Skewness1.2200186
Sum71146269
Variance1322302.1
MonotonicityNot monotonic
2025-12-10T12:25:13.471634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1164.47495
 
1.0%
460.4612
 
< 0.1%
1360.4512
 
< 0.1%
1109.0312
 
< 0.1%
1151.712
 
< 0.1%
148.528
 
< 0.1%
1124.028
 
< 0.1%
1961.738
 
< 0.1%
446.318
 
< 0.1%
1353.868
 
< 0.1%
Other values (12193)49417
98.8%
ValueCountFrequency (%)
0.234
< 0.1%
0.344
< 0.1%
0.544
< 0.1%
0.564
< 0.1%
0.774
< 0.1%
0.958
< 0.1%
1.24
< 0.1%
1.234
< 0.1%
1.34
< 0.1%
1.334
< 0.1%
ValueCountFrequency (%)
4998.074
< 0.1%
4997.14
< 0.1%
4997.054
< 0.1%
4992.254
< 0.1%
4990.914
< 0.1%
4987.194
< 0.1%
4986.034
< 0.1%
4984.823
< 0.1%
4983.864
< 0.1%
4982.574
< 0.1%

Credit_Utilization_Ratio
Real number (ℝ)

Unique 

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.279581
Minimum20.509652
Maximum48.540663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:13.663449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.509652
5-th percentile24.274339
Q128.06104
median32.28039
Q336.468591
95-th percentile40.244882
Maximum48.540663
Range28.031011
Interquartile range (IQR)8.4075506

Descriptive statistics

Standard deviation5.1062377
Coefficient of variation (CV)0.15818786
Kurtosis-0.94942073
Mean32.279581
Median Absolute Deviation (MAD)4.2018629
Skewness0.037595743
Sum1613979.1
Variance26.073664
MonotonicityNot monotonic
2025-12-10T12:25:13.863199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.030401861
 
< 0.1%
33.05311451
 
< 0.1%
33.811894121
 
< 0.1%
32.430559021
 
< 0.1%
25.92682171
 
< 0.1%
30.116600451
 
< 0.1%
30.996423741
 
< 0.1%
33.875167221
 
< 0.1%
35.229707331
 
< 0.1%
35.685835951
 
< 0.1%
Other values (49990)49990
> 99.9%
ValueCountFrequency (%)
20.509652061
< 0.1%
20.620017321
< 0.1%
20.739225491
< 0.1%
20.800586851
< 0.1%
20.839226381
< 0.1%
20.919647981
< 0.1%
21.119669111
< 0.1%
21.140201931
< 0.1%
21.181581511
< 0.1%
21.187105261
< 0.1%
ValueCountFrequency (%)
48.540663091
< 0.1%
48.228714011
< 0.1%
48.152777491
< 0.1%
48.096457271
< 0.1%
48.065280661
< 0.1%
47.288987261
< 0.1%
47.230103591
< 0.1%
47.163172451
< 0.1%
46.977776381
< 0.1%
46.947533251
< 0.1%

Credit_History_Months
Real number (ℝ)

High correlation 

Distinct399
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.04992
Minimum10
Maximum408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:14.073105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile73
Q1160
median225
Q3298
95-th percentile385
Maximum408
Range398
Interquartile range (IQR)138

Descriptive statistics

Standard deviation95.002194
Coefficient of variation (CV)0.41841985
Kurtosis-0.69698829
Mean227.04992
Median Absolute Deviation (MAD)69
Skewness-0.043285361
Sum11352496
Variance9025.4169
MonotonicityNot monotonic
2025-12-10T12:25:14.279777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2254654
 
9.3%
241254
 
0.5%
193254
 
0.5%
223252
 
0.5%
235252
 
0.5%
222250
 
0.5%
198248
 
0.5%
229242
 
0.5%
217241
 
0.5%
199238
 
0.5%
Other values (389)43115
86.2%
ValueCountFrequency (%)
1013
 
< 0.1%
1116
 
< 0.1%
1233
0.1%
1338
0.1%
1427
0.1%
1533
0.1%
1625
0.1%
1733
0.1%
1851
0.1%
1951
0.1%
ValueCountFrequency (%)
40814
 
< 0.1%
40715
 
< 0.1%
40643
 
0.1%
40591
0.2%
404118
0.2%
403158
0.3%
402151
0.3%
401117
0.2%
400108
0.2%
39998
0.2%

Payment_of_Min_Amount
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Yes
26158 
No
17849 
NM
5993 

Length

Max length3
Median length3
Mean length2.52316
Min length2

Characters and Unicode

Total characters126158
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Yes26158
52.3%
No17849
35.7%
NM5993
 
12.0%

Length

2025-12-10T12:25:14.462848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:14.579500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes26158
52.3%
no17849
35.7%
nm5993
 
12.0%

Most occurring characters

ValueCountFrequency (%)
Y26158
20.7%
e26158
20.7%
s26158
20.7%
N23842
18.9%
o17849
14.1%
M5993
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)126158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y26158
20.7%
e26158
20.7%
s26158
20.7%
N23842
18.9%
o17849
14.1%
M5993
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)126158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y26158
20.7%
e26158
20.7%
s26158
20.7%
N23842
18.9%
o17849
14.1%
M5993
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)126158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y26158
20.7%
e26158
20.7%
s26158
20.7%
N23842
18.9%
o17849
14.1%
M5993
 
4.8%

Total_EMI_per_month
Real number (ℝ)

Zeros 

Distinct10867
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.57743
Minimum0
Maximum392.06015
Zeros5002
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:14.735276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132.222388
median74.733349
Q3176.15749
95-th percentile392.06015
Maximum392.06015
Range392.06015
Interquartile range (IQR)143.9351

Descriptive statistics

Standard deviation116.98867
Coefficient of variation (CV)0.97835075
Kurtosis0.21593906
Mean119.57743
Median Absolute Deviation (MAD)55.075845
Skewness1.1451811
Sum5978871.6
Variance13686.349
MonotonicityNot monotonic
2025-12-10T12:25:14.940434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05002
 
10.0%
392.0601473933
 
7.9%
113.83339084
 
< 0.1%
16.415451664
 
< 0.1%
35.104022614
 
< 0.1%
49.574949214
 
< 0.1%
18.816214574
 
< 0.1%
261.20121784
 
< 0.1%
50.960878284
 
< 0.1%
16.185939914
 
< 0.1%
Other values (10857)41033
82.1%
ValueCountFrequency (%)
05002
10.0%
4.4628374674
 
< 0.1%
4.7131835724
 
< 0.1%
4.8656896774
 
< 0.1%
4.9161385424
 
< 0.1%
5.1384846964
 
< 0.1%
5.2184663594
 
< 0.1%
5.249273274
 
< 0.1%
5.2622910484
 
< 0.1%
5.3510861514
 
< 0.1%
ValueCountFrequency (%)
392.0601473933
7.9%
391.62936963
 
< 0.1%
391.24556963
 
< 0.1%
391.04682094
 
< 0.1%
390.8549094
 
< 0.1%
390.70543594
 
< 0.1%
390.58879944
 
< 0.1%
390.45128824
 
< 0.1%
390.24329222
 
< 0.1%
389.83719673
 
< 0.1%

Amount_invested_monthly
Real number (ℝ)

High correlation 

Distinct45450
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.98437
Minimum0
Maximum1908.1244
Zeros106
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:15.146831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.687205
Q177.031011
median129.03212
Q3220.5592
95-th percentile588.71397
Maximum1908.1244
Range1908.1244
Interquartile range (IQR)143.52818

Descriptive statistics

Standard deviation188.63977
Coefficient of variation (CV)0.99817654
Kurtosis9.1712604
Mean188.98437
Median Absolute Deviation (MAD)62.34207
Skewness2.636989
Sum9449218.5
Variance35584.961
MonotonicityNot monotonic
2025-12-10T12:25:15.362976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.03211874446
 
8.9%
0106
 
0.2%
148.23393791
 
< 0.1%
39.082510891
 
< 0.1%
39.684018421
 
< 0.1%
251.62736881
 
< 0.1%
72.680145331
 
< 0.1%
153.53448761
 
< 0.1%
397.50365351
 
< 0.1%
453.61513061
 
< 0.1%
Other values (45440)45440
90.9%
ValueCountFrequency (%)
0106
0.2%
10.004221751
 
< 0.1%
10.025344461
 
< 0.1%
10.036368011
 
< 0.1%
10.048056451
 
< 0.1%
10.051056951
 
< 0.1%
10.096769461
 
< 0.1%
10.131528491
 
< 0.1%
10.219228411
 
< 0.1%
10.226474261
 
< 0.1%
ValueCountFrequency (%)
1908.12441
< 0.1%
1867.8370271
< 0.1%
1813.0335881
< 0.1%
1801.35821
< 0.1%
1757.0248361
< 0.1%
1737.5200761
< 0.1%
1690.3242341
< 0.1%
1679.3399611
< 0.1%
1657.1763991
< 0.1%
1657.1382121
< 0.1%

Payment_Behaviour_lavel
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Low
24783 
High
21417 
Unknown
3800 

Length

Max length7
Median length4
Mean length3.73234
Min length3

Characters and Unicode

Total characters186617
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowHigh
3rd rowLow
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
Low24783
49.6%
High21417
42.8%
Unknown3800
 
7.6%

Length

2025-12-10T12:25:15.563534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:15.672414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low24783
49.6%
high21417
42.8%
unknown3800
 
7.6%

Most occurring characters

ValueCountFrequency (%)
o28583
15.3%
w28583
15.3%
L24783
13.3%
H21417
11.5%
i21417
11.5%
g21417
11.5%
h21417
11.5%
n11400
 
6.1%
U3800
 
2.0%
k3800
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)186617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o28583
15.3%
w28583
15.3%
L24783
13.3%
H21417
11.5%
i21417
11.5%
g21417
11.5%
h21417
11.5%
n11400
 
6.1%
U3800
 
2.0%
k3800
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)186617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o28583
15.3%
w28583
15.3%
L24783
13.3%
H21417
11.5%
i21417
11.5%
g21417
11.5%
h21417
11.5%
n11400
 
6.1%
U3800
 
2.0%
k3800
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)186617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o28583
15.3%
w28583
15.3%
L24783
13.3%
H21417
11.5%
i21417
11.5%
g21417
11.5%
h21417
11.5%
n11400
 
6.1%
U3800
 
2.0%
k3800
 
2.0%

Payment_Behaviour_size
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Small
18345 
Medium
15759 
Large
12096 
Unknown
3800 

Length

Max length7
Median length5
Mean length5.46718
Min length5

Characters and Unicode

Total characters273359
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowLarge

Common Values

ValueCountFrequency (%)
Small18345
36.7%
Medium15759
31.5%
Large12096
24.2%
Unknown3800
 
7.6%

Length

2025-12-10T12:25:15.822384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:15.950562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
small18345
36.7%
medium15759
31.5%
large12096
24.2%
unknown3800
 
7.6%

Most occurring characters

ValueCountFrequency (%)
l36690
13.4%
m34104
12.5%
a30441
11.1%
e27855
10.2%
S18345
 
6.7%
M15759
 
5.8%
d15759
 
5.8%
i15759
 
5.8%
u15759
 
5.8%
L12096
 
4.4%
Other values (7)50792
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)273359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l36690
13.4%
m34104
12.5%
a30441
11.1%
e27855
10.2%
S18345
 
6.7%
M15759
 
5.8%
d15759
 
5.8%
i15759
 
5.8%
u15759
 
5.8%
L12096
 
4.4%
Other values (7)50792
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)273359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l36690
13.4%
m34104
12.5%
a30441
11.1%
e27855
10.2%
S18345
 
6.7%
M15759
 
5.8%
d15759
 
5.8%
i15759
 
5.8%
u15759
 
5.8%
L12096
 
4.4%
Other values (7)50792
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)273359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l36690
13.4%
m34104
12.5%
a30441
11.1%
e27855
10.2%
S18345
 
6.7%
M15759
 
5.8%
d15759
 
5.8%
i15759
 
5.8%
u15759
 
5.8%
L12096
 
4.4%
Other values (7)50792
18.6%

Monthly_Balance
Real number (ℝ)

High correlation 

Distinct49433
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402.31557
Minimum0.10340223
Maximum1606.5182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-12-10T12:25:16.110483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10340223
5-th percentile176.64497
Q1271.11856
median336.98458
Q3468.57122
95-th percentile861.95932
Maximum1606.5182
Range1606.4148
Interquartile range (IQR)197.45265

Descriptive statistics

Standard deviation212.58327
Coefficient of variation (CV)0.52839931
Kurtosis2.9452848
Mean402.31557
Median Absolute Deviation (MAD)83.224889
Skewness1.5952097
Sum20115778
Variance45191.646
MonotonicityNot monotonic
2025-12-10T12:25:16.312351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
336.9845816568
 
1.1%
186.26670211
 
< 0.1%
361.44400391
 
< 0.1%
264.67544621
 
< 0.1%
343.82687321
 
< 0.1%
485.29843371
 
< 0.1%
303.35508331
 
< 0.1%
452.30230681
 
< 0.1%
421.44796451
 
< 0.1%
854.2260271
 
< 0.1%
Other values (49423)49423
98.8%
ValueCountFrequency (%)
0.10340223121
< 0.1%
0.25554996731
< 0.1%
0.72967956171
< 0.1%
0.77386013141
< 0.1%
0.91781270831
< 0.1%
1.0845519681
< 0.1%
1.1684068431
< 0.1%
1.3258016021
< 0.1%
1.3626364711
< 0.1%
1.3734121241
< 0.1%
ValueCountFrequency (%)
1606.5181921
< 0.1%
1566.1255721
< 0.1%
1553.8416361
< 0.1%
1553.3866471
< 0.1%
1550.852021
< 0.1%
1548.2041891
< 0.1%
1546.2647171
< 0.1%
1540.9359721
< 0.1%
1496.4088691
< 0.1%
1496.1139811
< 0.1%

Credit_Mix
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Standard
22814 
Good
15237 
Bad
11949 

Length

Max length8
Median length4
Mean length5.58614
Min length3

Characters and Unicode

Total characters279307
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard22814
45.6%
Good15237
30.5%
Bad11949
23.9%

Length

2025-12-10T12:25:16.507827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-10T12:25:16.612527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
standard22814
45.6%
good15237
30.5%
bad11949
23.9%

Most occurring characters

ValueCountFrequency (%)
d72814
26.1%
a57577
20.6%
o30474
10.9%
S22814
 
8.2%
t22814
 
8.2%
n22814
 
8.2%
r22814
 
8.2%
G15237
 
5.5%
B11949
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)279307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d72814
26.1%
a57577
20.6%
o30474
10.9%
S22814
 
8.2%
t22814
 
8.2%
n22814
 
8.2%
r22814
 
8.2%
G15237
 
5.5%
B11949
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)279307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d72814
26.1%
a57577
20.6%
o30474
10.9%
S22814
 
8.2%
t22814
 
8.2%
n22814
 
8.2%
r22814
 
8.2%
G15237
 
5.5%
B11949
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)279307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d72814
26.1%
a57577
20.6%
o30474
10.9%
S22814
 
8.2%
t22814
 
8.2%
n22814
 
8.2%
r22814
 
8.2%
G15237
 
5.5%
B11949
 
4.3%

Interactions

2025-12-10T12:25:01.840917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:08.471980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:11.374338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:14.204869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:17.474086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:24:31.438639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:34.730313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:37.968774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:41.217458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:44.304482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:47.203541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:50.485094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:53.667648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:56.931051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:00.586577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:04.019799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:10.375798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:13.240438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:16.437435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:19.301269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:22.417767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:25.655421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:28.453570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:31.600429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:34.886824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:38.122237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:41.379589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:44.449371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:47.361804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:50.648036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:53.855662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:57.128319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:00.748827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:04.204212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:10.535293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:13.389452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:16.599251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:19.440705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:22.587582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:25.827606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:28.596191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:31.753735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:35.104103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:38.270066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:41.537804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:44.608649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:47.520964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:50.817360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:54.036669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:57.619977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:00.914060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:04.375737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:10.687982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:13.539324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:16.755476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:19.599193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:22.755270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:25.980506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:28.746899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:31.911805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:35.268947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:38.417943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:41.715367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:44.799627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:47.691274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:50.970585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:54.219364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:57.815928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:01.096840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:04.561957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:10.856966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:13.707924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:16.940622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:19.757064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:22.922698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:26.155688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:28.906024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:32.084487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:35.490933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:38.586763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:41.903620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:44.968850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:47.868539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:51.142808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:54.399301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:25:04.745291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:11.033595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:13.873904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:17.125420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:19.937817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:24:26.324031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:29.077978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:32.260180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:35.664879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:38.768067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:42.101536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:45.132774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:48.049703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:51.305906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:54.586765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:58.183829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:24:11.203516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:24:17.295592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:20.113130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:23.538263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:24:29.259382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:24:38.932299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-10T12:24:45.291768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:48.225799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:51.483886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:54.831180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:24:58.372430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-10T12:25:01.649832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-10T12:25:16.778715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAmount_invested_monthlyAnnual_IncomeAuto LoanChanged_Credit_LimitCredit-Builder LoanCredit_History_MonthsCredit_MixCredit_Utilization_RatioDebt Consolidation LoanDelay_from_due_dateHome Equity LoanInterest_RateMonthly_BalanceMonthly_Inhand_SalaryMortgage LoanNum_Bank_AccountsNum_Credit_CardNum_Credit_InquiriesNum_of_Delayed_PaymentNum_of_LoanNum_of_Loan_TypesOccupationOutstanding_DebtPayday LoanPayment_Behaviour_lavelPayment_Behaviour_sizePayment_of_Min_AmountPersonal LoanStudent LoanTotal_EMI_per_month
Age1.0000.0320.0460.098-0.0960.0930.1430.1900.0160.093-0.1060.094-0.1340.0740.0400.093-0.118-0.089-0.147-0.105-0.125-0.1210.027-0.1320.0860.0270.0220.1900.0970.099-0.049
Amount_invested_monthly0.0321.0000.5770.060-0.0940.0650.1680.1360.0160.054-0.1470.059-0.185-0.0130.5590.051-0.166-0.123-0.161-0.158-0.152-0.1500.009-0.1720.0650.1850.0620.1160.0600.0570.280
Annual_Income0.0460.5771.0000.000-0.1330.0080.2370.0000.1200.000-0.2230.000-0.2700.5590.8650.007-0.249-0.186-0.236-0.228-0.212-0.2110.003-0.2500.0030.0000.0000.0070.0000.0000.439
Auto Loan0.0980.0600.0001.0000.1520.0940.2390.2580.0560.0770.1970.0680.2350.1770.1020.0720.1980.1770.0060.1830.3750.4400.0200.2650.0870.0310.0290.1920.0820.0680.209
Changed_Credit_Limit-0.096-0.094-0.1330.1521.0000.146-0.3430.411-0.0370.1310.2470.1500.316-0.177-0.1270.1410.2720.1770.3240.2440.2650.2590.0200.2800.1350.0300.0240.3400.1440.1410.081
Credit-Builder Loan0.0930.0650.0080.0940.1461.0000.2450.2710.0590.0720.1940.1020.2370.1990.1290.0770.2060.1840.0050.1950.3900.4510.0220.2690.0740.0340.0330.1960.0810.0730.206
Credit_History_Months0.1430.1680.2370.239-0.3430.2451.0000.4790.0630.231-0.4610.227-0.5450.3440.2240.240-0.466-0.377-0.568-0.435-0.533-0.5170.027-0.5660.2410.0570.0450.3970.2370.238-0.170
Credit_Mix0.1900.1360.0000.2580.4110.2710.4791.0000.0870.2450.5860.2580.6280.2920.2680.2650.6130.4650.0000.5860.5090.4390.0300.5850.2560.0640.0660.5450.2540.2490.152
Credit_Utilization_Ratio0.0160.0160.1200.056-0.0370.0590.0630.0871.0000.058-0.0640.060-0.0610.1840.1140.058-0.067-0.050-0.064-0.062-0.094-0.0880.000-0.0700.0500.0940.0690.0720.0520.0560.006
Debt Consolidation Loan0.0930.0540.0000.0770.1310.0720.2310.2450.0581.0000.2020.0830.2200.1860.1180.0700.1870.1620.0100.1610.3730.4310.0260.2470.0790.0360.0370.1870.0760.0530.208
Delay_from_due_date-0.106-0.147-0.2230.1970.2470.194-0.4610.586-0.0640.2021.0000.2020.567-0.303-0.2040.2040.5660.4350.4860.5180.4240.4120.0230.5150.1970.0520.0440.3570.2040.1970.122
Home Equity Loan0.0940.0590.0000.0680.1500.1020.2270.2580.0600.0830.2021.0000.2330.1830.1080.0800.1960.1720.0000.1870.3890.4490.0290.2580.0810.0260.0190.1940.0870.0700.215
Interest_Rate-0.134-0.185-0.2700.2350.3160.237-0.5450.628-0.0610.2200.5670.2331.000-0.347-0.2530.2300.5830.4540.5850.5390.4930.4830.0270.6030.2260.0620.0540.4450.2260.2210.126
Monthly_Balance0.074-0.0130.5590.177-0.1770.1990.3440.2920.1840.186-0.3030.183-0.3471.0000.5440.183-0.314-0.249-0.329-0.295-0.453-0.4370.011-0.3500.1770.3390.2130.2300.1810.1750.034
Monthly_Inhand_Salary0.0400.5590.8650.102-0.1270.1290.2240.2680.1140.118-0.2040.108-0.2530.5441.0000.109-0.228-0.168-0.220-0.212-0.196-0.1940.023-0.2340.1080.1930.1510.2110.1070.1060.417
Mortgage Loan0.0930.0510.0070.0720.1410.0770.2400.2650.0580.0700.2040.0800.2300.1830.1091.0000.2070.1750.0000.1800.3870.4350.0420.2660.0680.0260.0280.1900.0860.0720.221
Num_Bank_Accounts-0.118-0.166-0.2490.1980.2720.206-0.4660.613-0.0670.1870.5660.1960.583-0.314-0.2280.2071.0000.4200.4870.5440.4260.4140.0280.4920.2000.0590.0510.4050.2000.1810.097
Num_Credit_Card-0.089-0.123-0.1860.1770.1770.184-0.3770.465-0.0500.1620.4350.1720.454-0.249-0.1680.1750.4201.0000.3980.3750.3500.3410.0260.4470.1790.0430.0420.2890.1680.1660.092
Num_Credit_Inquiries-0.147-0.161-0.2360.0060.3240.005-0.5680.000-0.0640.0100.4860.0000.585-0.329-0.2200.0000.4870.3981.0000.4440.4910.4780.0000.5620.0090.0000.0000.0030.0090.0040.148
Num_of_Delayed_Payment-0.105-0.158-0.2280.1830.2440.195-0.4350.586-0.0620.1610.5180.1870.539-0.295-0.2120.1800.5440.3750.4441.0000.4030.3920.0190.4570.1690.0470.0430.3750.1700.1740.097
Num_of_Loan-0.125-0.152-0.2120.3750.2650.390-0.5330.509-0.0940.3730.4240.3890.493-0.453-0.1960.3870.4260.3500.4910.4031.0000.8710.0270.5190.3840.0520.0430.3550.3790.3740.466
Num_of_Loan_Types-0.121-0.150-0.2110.4400.2590.451-0.5170.439-0.0880.4310.4120.4490.483-0.437-0.1940.4350.4140.3410.4780.3920.8711.0000.0290.5050.4390.0470.0390.3150.4440.4230.457
Occupation0.0270.0090.0030.0200.0200.0220.0270.0300.0000.0260.0230.0290.0270.0110.0230.0420.0280.0260.0000.0190.0270.0291.0000.0280.0090.0070.0110.0130.0230.0050.025
Outstanding_Debt-0.132-0.172-0.2500.2650.2800.269-0.5660.585-0.0700.2470.5150.2580.603-0.350-0.2340.2660.4920.4470.5620.4570.5190.5050.0281.0000.2580.0630.0560.3760.2570.2560.145
Payday Loan0.0860.0650.0030.0870.1350.0740.2410.2560.0500.0790.1970.0810.2260.1770.1080.0680.2000.1790.0090.1690.3840.4390.0090.2581.0000.0240.0290.1990.0710.0720.209
Payment_Behaviour_lavel0.0270.1850.0000.0310.0300.0340.0570.0640.0940.0360.0520.0260.0620.3390.1930.0260.0590.0430.0000.0470.0520.0470.0070.0630.0241.0000.7290.0540.0230.0280.104
Payment_Behaviour_size0.0220.0620.0000.0290.0240.0330.0450.0660.0690.0370.0440.0190.0540.2130.1510.0280.0510.0420.0000.0430.0430.0390.0110.0560.0290.7291.0000.0570.0250.0340.084
Payment_of_Min_Amount0.1900.1160.0070.1920.3400.1960.3970.5450.0720.1870.3570.1940.4450.2300.2110.1900.4050.2890.0030.3750.3550.3150.0130.3760.1990.0540.0571.0000.1900.1850.106
Personal Loan0.0970.0600.0000.0820.1440.0810.2370.2540.0520.0760.2040.0870.2260.1810.1070.0860.2000.1680.0090.1700.3790.4440.0230.2570.0710.0230.0250.1901.0000.0750.204
Student Loan0.0990.0570.0000.0680.1410.0730.2380.2490.0560.0530.1970.0700.2210.1750.1060.0720.1810.1660.0040.1740.3740.4230.0050.2560.0720.0280.0340.1850.0751.0000.205
Total_EMI_per_month-0.0490.2800.4390.2090.0810.206-0.1700.1520.0060.2080.1220.2150.1260.0340.4170.2210.0970.0920.1480.0970.4660.4570.0250.1450.2090.1040.0840.1060.2040.2051.000

Missing values

2025-12-10T12:25:05.553716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-10T12:25:06.136394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDebt Consolidation LoanHome Equity LoanStudent LoanPayday LoanPersonal LoanAuto LoanMortgage LoanCredit-Builder LoanNum_of_Loan_TypesDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioCredit_History_MonthsPayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_Behaviour_lavelPayment_Behaviour_sizeMonthly_BalanceCredit_Mix
023Scientist19114.121824.84333334340100110143711.272022809.9835.030402273.0No49.574949236.642682LowSmall186.266702Good
124Scientist19114.121824.84333334340100110143913.274809.9833.053114274.0No49.57494921.465380HighMedium361.444004Good
224Scientist19114.121824.8433333434010011014-1412.274809.9833.811894225.0No49.574949148.233938LowMedium264.675446Good
334Scientist19114.123086.30500034340100110144511.274809.9832.430559276.0No49.57494939.082511HighMedium343.826873Good
428Unknown34847.843037.9866672461000000011315.425605.0325.926822327.0No18.81621539.684018HighLarge485.298434Good
528Teacher34847.843037.9866672461000000011335.425605.0330.116600328.0No18.816215251.627369LowLarge303.355083Good
628Teacher34847.843037.98666724610000000113145.425605.0330.996424329.0No18.81621572.680145HighLarge452.302307Good
728Teacher34847.843037.98666724610000000113147.425605.0333.875167330.0No18.816215153.534488UnknownUnknown421.447964Good
835Engineer143162.643086.30500015830000010018147.1031303.0135.229707221.0No246.992319397.503654LowMedium854.226027Good
935Engineer143162.6412187.2200001583000001001632.1031303.0135.685836222.0No246.992319453.615131LowLarge788.114550Good
AgeOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDebt Consolidation LoanHome Equity LoanStudent LoanPayday LoanPersonal LoanAuto LoanMortgage LoanCredit-Builder LoanNum_of_Loan_TypesDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioCredit_History_MonthsPayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_Behaviour_lavelPayment_Behaviour_sizeMonthly_BalanceCredit_Mix
4999050Writer37188.103097.008333141330110001037125.383620.6425.708414367.0No84.205949183.365628LowLarge312.129256Good
4999134Writer37188.103097.00833314530110001033125.383620.6436.498383368.0No392.060147238.399383LowLarge257.095501Good
4999229Architect20002.881929.906667108295001011104332518.3193571.7032.39128876.0Yes60.964772107.210742LowSmall314.815153Bad
4999329Architect20002.881929.906667108295001011104332518.31123571.7037.52851177.0Yes60.96477271.794421LowSmall350.231473Bad
4999429Unknown20002.881929.906667108295001011104332218.31123571.7027.02781278.0Yes60.96477250.846847HighSmall341.179047Bad
4999534Architect20002.881929.906667108295001011104332518.31123571.7034.780553225.0Yes60.964772146.486325LowSmall275.539570Bad
4999625Mechanic39628.993086.3050004673001001002201411.507502.3827.758522383.0NM35.104023181.442999LowSmall409.394562Good
4999725Mechanic39628.993359.415833467200100100223513.507502.3836.858542384.0No35.104023129.032119LowLarge349.726332Good
4999825Mechanic39628.993086.3050004673001001002211411.507502.3839.139840385.0No35.10402397.598580HighSmall463.238981Good
4999925Mechanic39628.993359.415833467200100100222511.507502.3834.108530386.0No35.104023220.457878LowMedium360.379683Good